Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT Chapter 5 Robust Performance Tailoring with Tuning - SSL - MIT
updating for tuning is developed and demonstrated on the SCI development model. The tuning methods are compared to one another for performance and testing cost over a large sample of hardware simulations, and it is shown that only isoperformance tuning consistently requires a small number of hardware tests and is successful across the sample space. 4.1 Tuning Formulation In this thesis, dynamic tuning is defined as adjustments made to the hardware once it is built to affect the performance and bring it within requirements. Consider a situation in which models predict that the system meets performance requirements across most of the uncertainty space, but not at the extremes. If only one such system is built, there is a possibility that the physical system may lie outside the area in which the RPT design meets requirements. However, if there are tuning adjustments that can be made to the hardware at this stage to affect the performance, it may be possible to improve the system performance to within the desired range. Due to model inaccuracies and manufacturing discrepancies, situations such as this arise frequently in practice. Engineers often make ad hoc adjustments to hardware to improve performance or bring a component or entire system within specifications. However, this type of tuning is not formalized, and it is difficult to find references on these practices. Dynamic tuning is a hardware-based procedure, and therefore, the tuning parame- ters must be chosen carefully. As in the case of performance tailoring, the parameters must have some effect on the performance metric. The range of this effect, or the difference between the tuned and untuned performance, is referred to as the tuning authority. In addition, the parameters must be easy to adjust on the hardware, signif- icantly limiting the possible design variables compared to those available for tailoring. For example, although it has been shown previously that the cross-sectional diameters of the truss members are good tailoring parameters for the SCI development model, they are not well-suited for tuning as there is not an easy way to change these values 108
on a physical truss. Since tailoring takes place on models it is easy to try a range of truss diameters and optimize the design in this manner. However, tuning with these parameters requires physically cutting into the existing truss and replacing members. This procedure is expensive and may have undesired global effects on the performance resulting in mission delays. One example of dynamic tuning on hardware is found in a recent paper by Glaese and Bales [46]. The authors study the effects of a range of structural adjustments to a gossamer structure. In the paper, they call the process dynamic tailoring because the goal is to affect the dynamic performance of the membrane. However, in the context of this thesis, their efforts are classified as dynamic tuning since the authors make adjustments to the hardware itself. A 80-inch major diameter, 8-inch minor diameter, pre-formed Kapton torus is the structure used to demonstrate the dynamic tuning. The authors choose four possible tuning parameters: inert masses added at random locations on the structure to minimize the disturbance forces by increasing the effective impedance, tuned mass dampers to reduce narrow-band disturbance effects, piezo-electro actuators and sensors that enhance broadband structural damping, and shunts added to the piezo-electric elements to actively control the flow of energy in the structure. All of these techniques are good examples of tuning parameters since they can be added to the structure and adjusted with minimal disruption to the hardware itself. The formulation of the tuning optimization problem is similar to performance tailoring with the major distinction being that the system performance is now a function of the tailoring, �x, uncertainty, �p, and tuning parameters, �y, but the tuning parameters are the only design variables: min f (˜x, �y, ˜p) �y (4.1) s.t. �g (˜x, �y) ≤ 0 where ˜x and ˜p are the actual values of the tailoring and uncertainty parameters realized in the hardware and g (˜x, �y) are constraints on the design variables. The 109
- Page 57 and 58: at least locally optimal, and the s
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- Page 61 and 62: and the RMS OPD is computed using E
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- Page 77 and 78: ic, σz(�x, �p), that is depend
- Page 79 and 80: Magnitude, OPD/F x [µm/N] Magnitud
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- Page 85 and 86: tion: ∂hi (z,�x, �pi) ∂�x
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- Page 97 and 98: Norm. Cum. Var. [µm 2 ] PSD [µm 2
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- Page 113 and 114: m 2 [kg] J ∗ # # time y ∗ [kg]
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- Page 119 and 120: Norm. Cum. Var. [µm 2 ] PSD [µm 2
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- Page 133 and 134: p [GPa] y ∗ [kg] Performance [µm
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on a physical truss. Since tailoring takes place on models it is easy to try a range of<br />
truss diameters and optimize the design in this manner. However, tuning <strong>with</strong> these<br />
parameters requires physically cutting into the existing truss and replacing members.<br />
This procedure is expensive and may have undesired global effects on the performance<br />
resulting in mission delays.<br />
One example of dynamic tuning on hardware is found in a recent paper by Glaese<br />
and Bales [46]. The authors study the effects of a range of structural adjustments to<br />
a gossamer structure. In the paper, they call the process dynamic tailoring because<br />
the goal is to affect the dynamic performance of the membrane. However, in the<br />
context of this thesis, their efforts are classified as dynamic tuning since the authors<br />
make adjustments to the hardware itself. A 80-inch major diameter, 8-inch minor<br />
diameter, pre-formed Kapton torus is the structure used to demonstrate the dynamic<br />
tuning. The authors choose four possible tuning parameters: inert masses added at<br />
random locations on the structure to minimize the disturbance forces by increasing the<br />
effective impedance, tuned mass dampers to reduce narrow-band disturbance effects,<br />
piezo-electro actuators and sensors that enhance broadband structural damping, and<br />
shunts added to the piezo-electric elements to actively control the flow of energy in the<br />
structure. All of these techniques are good examples of tuning parameters since they<br />
can be added to the structure and adjusted <strong>with</strong> minimal disruption to the hardware<br />
itself.<br />
The formulation of the tuning optimization problem is similar to performance<br />
tailoring <strong>with</strong> the major distinction being that the system performance is now a<br />
function of the tailoring, �x, uncertainty, �p, and tuning parameters, �y, but the tuning<br />
parameters are the only design variables:<br />
min f (˜x, �y, ˜p)<br />
�y<br />
(4.1)<br />
s.t. �g (˜x, �y) ≤ 0<br />
where ˜x and ˜p are the actual values of the tailoring and uncertainty parameters<br />
realized in the hardware and g (˜x, �y) are constraints on the design variables. The<br />
109
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